.. .. image:: _static/goes2go_logo.png .. This drop-shadow glow on the logo helps when in Darkmode .. raw:: html ======================= GOES-2-Go Documentation ======================= GOES-2-Go is a python package that helps you download GOES-R series (GOES-East/16 and GOES-West/17/18) NetCDF files from the `Amazon Web Services `_ archive and provides RGB recipes for various RGB products. .. toctree:: :maxdepth: 1 /user_guide/index /reference_guide/index Install ------- The easiest way to install ``goes2go`` and its dependencies is with Conda from conda-forge. .. code:: bash conda install -c conda-forge goes2go You may also create the provided Conda environment file `environment.yml `_. .. code:: bash # Create the environment conda env create -f environment.yml # Update the environment conda env update -f environment.yml # Activate the environment conda activate goes2go Alternatively, ``goes2go`` is published on PyPI and you can install it with pip, but it requires some additional dependencies that you will have to install yourself: - Python 3.8+ - Cartopy, which requires GEOS and Proj. - MetPy - Optional: Carpenter Workshop When those are installed within your environment, then you can install GOES-2-go with pip. .. code:: bash # Latest published version pip install goes2go # ~~ or ~~ # Most recent changes pip install git+https://github.com/blaylockbk/goes2go.git Capabilities ------------ Download and Read Data ^^^^^^^^^^^^^^^^^^^^^^ First, create a GOES object to specify the satellite, data product, and domain you are interested in. The example below downloads the Multi-Channel Cloud Moisture Imagery for CONUS. .. code-block:: python from goes2go import GOES # ABI Multi-Channel Cloud Moisture Imagry Product G = GOES(satellite=16, product="ABI-L2-MCMIP", domain='C') # Geostationary Lightning Mapper G = GOES(satellite=17, product="GLM-L2-LCFA", domain='C') # ABI Level 1b Data G = GOES(satellite=17, product="ABI-L1b-Rad", domain='F') .. note:: A complete listing of the products available are available at `here `_. There are methods to do the following: * List the available files for a time range * Download data to your local drive for a specified time range * Read the data into an xarray Dataset for a specific time .. code-block:: python # Produce a pandas DataFrame of the available files in a time range df = G.df(start='2022-07-04 01:00', end='2022-07-04 01:30') .. code-block:: python # Download and read the data as an xarray Dataset nearest a specific time ds = G.nearesttime('2022-01-01') .. code-block:: python # Download and read the latest data as an xarray Dataset ds = G.latest() .. code-block:: python # Download data for a specified time range G.timerange(start='2022-06-01 00:00', end='2022-06-01 01:00') # Download recent data for a specific interval G.timerange(recent='30min') - `📖 Download latest `_ - `📖 Download nearest time `_ - `📖 Download time series `_ RGB Recipes for ABI ^^^^^^^^^^^^^^^^^^^ Generate RGB arrays for different RGB products. Check out the following notebook for a demonstration: - `📖 RGB Recipes Docs `_ .. figure:: _static/TrueColor.png :class: img-fluid ABI TrueColor RGB image Field of View ^^^^^^^^^^^^^ Advanced Baseline Imager (ABI) """""""""""""""""""""""""""""" GOES-West is centered over -137 W and GOES-East is centered over -75 W. When GOES was being tested, it was in a "central" position, outlined in the dashed black line. Below is the ABI field of view for the full disk: - `📓 ABI field of view notebook `_ .. figure:: _static/ABI_field-of-view.png :class: img-fluid .. figure:: _static/ABI_field-of-view_16dom.png :class: img-fluid .. figure:: _static/ABI_field-of-view_17dom.png :class: img-fluid ABI full disk field of view Geostationary Lightning Mapper (GLM) """""""""""""""""""""""""""""""""""" The GLM field of view is slightly smaller and limited by a bounding box. The field of view can be estimated. - `📓 GLM field of view notebook `_ - `📓 More details on actual edges `_ .. figure:: _static/GLM_field-of-view.png :class: img-fluid Approximate GLM field of view Useful Links ------------ - `👨🏻‍💻 Brian's GitHub notebooks `_ - `🎠 Beginner's Guide `_ - `📔 GOES-R Series Data Book `_ - `💻 Rammb Slider GOES Viewer `_ - `🐍 Unidata Plot GOES Data `_ - `🗺 Plotting tips form geonetcast blog `_ - `🐍 glmtools `_ For useful tools for GLM data. - `⏲ Mesoscale Sector Historical Archive `_ shows maps of where mesoscale scans were at each time. - `🗺 CIMSS Gridding GLM Tool `_ 💾 Data Access ^^^^^^^^^^^^^^^^ Access GOES data through NOAA's Big Data Project partners - `Amazon Web Services `_ - `🌐 Brian's Interactive GOES Download Page `_ - `🗃 AWS GOES-16 S3 Explorer `_ - `🗃 AWS GOES-17 S3 Explorer `_ - `Microsoft Azure `_ (Only GOES-16??) - Google Cloud Platform (`GOES-16 `_ | `GOES-17 `_) - `Download GOES data with rclone `_ Data Quality ^^^^^^^^^^^^^ - `⛑ GOES-17 ABI Data Quality (CIMSS) `_